ArticleBioengineering (Basel, Switzerland)2024
A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Application of transformer attention mechanism-based multimodal deep learning model in the diagnosis of papillary thyroid carcinoma.BMC medical imaging · 2026Article
- A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.Scientific reports · 2026Article
- ThyroFusion: A Multi-modal Deep Learning Framework Integrating Vision and Language for Thyroid Nodule Malignancy Risk Assessment.Journal of imaging informatics in medicine · 2026Article
- Thyroid Nodule Detection and Classification on Small Datasets: An Ensemble Deep Learning Approach with Attention Mechanism and Focal Loss.Diagnostics (Basel, Switzerland) · 2026Article
- Adaptive Bandelet Transform and Transfer Learning for Geometry-Aware Thyroid Cancer Ultrasound Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-assisted risk stratification of thyroid nodules with atypia of undetermined significance.European thyroid journal · 2026Article
- Hybrid deep feature and machine learning framework for classification of thyroid nodules in ultrasound images.Frontiers in oncology · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging.IEEE open journal of engineering in medicine and biology · 2026Article
- A comprehensive model with dual plane ultrasound based deep learning radiomics to distinguish parotid pleomorphic adenoma from Warthin tumor.Frontiers in oncology · 2026Article
- Automated thyroid nodule classification in ultrasound imaging using a hybrid vision transformer and Wasserstein GAN with gradient penalty.Scientific reports · 2025Article
- Study on differentiating benign and malignant thyroid nodules based on CT multi-phase artificial intelligence models.Frontiers in endocrinology · 2025Article
- TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2024Article
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7 authors.
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Abstract
Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75-0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (
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